Fuzzy modelling to identify key drivers of ecological water quality to support decision and policy making |
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| Institution: | 1. Graduate Program in Environmental Engineering, Federal University of Ouro Preto, Minas Gerais, Brazil;2. Graduate Program in Social and Environmental Sustainability, Federal University of Ouro Preto, Minas Gerais, Brazil;1. Department of Electrical Power Systems, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;2. Department of Information Systems, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania;3. Intelligent Systems Laboratory, Centre for Applied Intelligent Systems Research, Halmstad University, Kristian IV:s väg 3, PO Box 823, S-301 18 Halmstad, Sweden;4. Marine Science and Technology Centre, Klaipeda University, Herkaus Manto 84, LT-92294 Klaipeda, Lithuania;5. Department of Marine Research, Environmental Protection Agency, Taikos Av. 26, LT-91144 Klaipeda, Lithuania;1. Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ciencias de la Vida, Campus Gustavo Galindo kilómetro 30.5 Vía Perimetral, PO Box 09-01-5863, Guayaquil, Ecuador;2. ESPOL, Centro del Agua y Desarrollo Sustentable, Campus Gustavo Galindo kilómetro 30.5 Vía Perimetral, PO Box 09-01-5863, Guayaquil, Ecuador;3. Royal Belgian Institute of Natural Sciences, Operational Directorate Natural Environment (OD Nature), Marine Ecology and Management, Gulledelle 100, 1200 Brussels, Belgium;4. Ghent University, Biology Department, Marine Biology Research Group, Krijgslaan 281/S8, 9000 Gent, Belgium;5. Vlaams Instituut Voor De Zee, InnovOcean Site, Wandelaarkaai 7, 8400 Oostende, Belgium;6. ESPOL, Facultad de Ingeniería Marítima, Ciencias Biológicas, Oceánicas y Recursos Naturales, Campus Gustavo Galindo kilómetro 30.5 Vía Perimetral, PO Box 09-01-5863, Guayaquil, Ecuador;7. ESPOL, Facultad de Ciencias Naturales y Matemáticas, Campus Gustavo Galindo kilómetro 30.5, Vía Perimetral, PO Box 09-01-5863, Guayaquil, Ecuador;1. The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8654, Japan |
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| Abstract: | Water quality modelling is an effective tool to investigate, describe and predict the ecological state of an aquatic ecosystem. Various environmental variables may simultaneously affect water quality. Appropriate selection of a limited number of key-variables facilitates cost-effective management of water resources. This paper aims to determine (and analyse the effect of) the major environmental variables predicting ecological water quality through the application of fuzzy models. In this study, a fuzzy logic methodology, previously applied to predict species distributions, was extended to model environmental effects on a whole community. In a second step, the developed models were applied in a more general water management context to support decision and policy making. A hill-climbing optimisation algorithm was applied to relate ecological water quality and environmental variables to the community indicator. The optimal model was selected based on the predictive performance (Cohen’s Kappa), ecological relevance and model’s interpretability. Moreover, a sensitivity analysis was performed as an extra element to analyse and evaluate the optimal model. The optimal model included the variables land use, chlorophyll and flow velocity. The variable selection method and sensitivity analysis indicated that land use influences ecological water quality the most and that it affects the effect of other variables on water quality to a high extent. The model outcome can support spatial planning related to land use in river basins and policy making related to flows and water quality standards. Fuzzy models are transparent to a wide range of users and therefore may stimulate communication between modellers, river managers, policy makers and stakeholders. |
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| Keywords: | Fuzzy logic Environmental variables Decision support systems River basin management |
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